Energy-saving optimization and hydraulic balance adjusting system for heating system

Abstract

This invention provides an energy-saving optimization and hydraulic balance regulation system for heating systems, relating to the field of heating system technology. It includes a data acquisition and detection module; a digital twin and simulation prediction module; a network-wide balancing module for adjusting the operating parameters of the heating system based on the output of the digital twin and simulation prediction module; an energy-saving control module; a terminal control module for adjusting indoor temperatures collected by a room temperature detection device; an operation management module; and a fault diagnosis and early warning module. This invention, by constructing a digital twin model of the heating system, reduces the probability of data distortion in the twin model and deviations from actual simulation predictions caused by complex underground or outdoor environments where pipe networks are often located, sensors are susceptible to low temperatures and corrosion, and data transmission is prone to delays or interruptions. Furthermore, the system directly transmits data to field equipment via a cloud-edge-device collaborative system, eliminating the need for manual relay and reducing delays and operational errors.

Description

Technical Field

[0001] This invention relates to the field of heating system technology, and in particular to a heating system energy-saving optimization and hydraulic balance regulation system. Background Technology

[0002] Heating systems are systems used to provide indoor warmth. Their main purpose is to maintain indoor temperature during the cold season and ensure the comfort of the living and working environment. The design and selection of heating systems depend on a variety of factors, including climate conditions, building characteristics, energy availability, economics, and environmental impact. Energy-saving optimization and hydraulic balance regulation systems are important means to improve heating efficiency and save energy consumption. Hydraulic imbalance refers to uneven heat distribution caused by unreasonable pipe network design, outdated regulation methods, or dynamic load changes, resulting in phenomena such as nearby areas being hot and distant areas being cold, or the upper part being hot and the lower part being cold.

[0003] Currently, some heating systems have introduced automated control equipment, such as installing balancing valves for initial adjustment and using climate compensators to adjust the water supply temperature based on outdoor temperature. However, these methods still have significant limitations. First, the adjustment process relies heavily on human experience and lacks scientific data support, making precise control difficult. Second, most systems are open-loop controls, unable to perceive the overall network operation status in real time, making it difficult to verify the effects of adjustments and easily leading to new imbalances. Third, existing systems have weak data acquisition capabilities, focusing only on key points such as heat source outlets and lacking comprehensive monitoring of branch pipelines and user-end room temperature, resulting in information blind spots. Finally, there is a lack of pre-emptive simulation capabilities for different adjustment schemes, making maintenance personnel hesitant to make adjustments, causing the system to operate in a suboptimal state for extended periods. Deploying sensor networks for data acquisition and combining them with a cloud-edge-device collaborative architecture for data analysis has already been applied in some smart energy projects. However, most existing technologies remain at the level of "data visualization" or "simple alarms," ​​and have not yet built a high-fidelity virtual model that can truly reflect the dynamic characteristics of the physical system. They are even less able to achieve quantitative simulation and optimization decisions for regulation strategies. In traditional heating systems, branch pipelines and user terminals in old residential areas often lack the foundation for sensor installation. Adding new sensors requires modification of the pipeline network and installation in households, which faces problems such as high costs and low resident cooperation. At the same time, the pipeline network is mostly located underground or in complex outdoor environments, and the sensors are susceptible to low temperatures and corrosion. Data transmission is prone to delays or interruptions, which directly leads to data distortion in the twin model and simulation prediction results deviating from reality.

[0004] Therefore, it is necessary to provide a new heating system energy-saving optimization and hydraulic balance regulation system to solve the above-mentioned technical problems. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a heating system energy-saving optimization and hydraulic balance regulation system.

[0006] The present invention provides an energy-saving optimization and hydraulic balance regulation system for a heating system, which includes a data acquisition and detection module for real-time acquisition of heating network operation data and uploading the data to a cloud platform via a wireless network; The digital twin and simulation prediction module is used to analyze and process the data collected by the data acquisition and detection module, and at the same time build a digital twin model of the heating system. The digital twin model is then used for real-time simulation and prediction to preview different adjustment schemes and operating strategies. The whole network balancing module is used to adjust the operating parameters of the heating system based on the output of the digital twin and simulation prediction module. At the same time, the "load prediction - valve opening" mapping model established by reinforcement learning algorithm is incorporated into it. By combining factors such as weather forecast and historical heating patterns, the reinforcement learning algorithm continuously optimizes the valve opening adjustment strategy. The energy-saving control module is used to generate the target flow rate of cold chain transportation at the station based on big data and machine learning algorithms, and dynamically determine the opening degree of the primary side regulating valve at the station according to the target flow rate, combined with the heating network simulation model and valve regulation characteristics. The terminal control module is used to automatically adjust the valve opening based on the indoor temperature collected by the room temperature detection device; The operation management module is used to make load forecasts based on historical heating data and weather conditions, and to adjust operation strategies and heating plans in a timely manner. This module includes a system-wide monitoring and alarm unit, a maintenance work order and equipment management unit, an energy consumption analysis and cost management unit, an operation strategy configuration and optimization unit, and a user service and interaction unit. The system-wide monitoring and alarm unit centrally presents scattered operation data and equipment status, while simultaneously capturing anomalies and issuing warnings in real time. The maintenance work order and equipment management unit transforms reactive fault repair into proactive preventative maintenance by systematically managing maintenance task processes and the entire equipment lifecycle. The energy consumption analysis and cost management unit transforms invisible energy consumption into data assets through multi-dimensional energy consumption statistics, accurate cost accounting, and quantification of energy-saving effects. The operation strategy configuration and optimization unit is responsible for storing and adjusting system operation strategies. The service and interaction unit connects operators and users, handles requests, and transmits information. The fault diagnosis and early warning module is used to diagnose potential faults in the pipeline network in real time by analyzing pipeline network operation data.

[0007] Preferably, the analysis and processing of the data collected by the data acquisition and detection module includes the following steps: S1. Data preprocessing: invalid data is removed from the raw data using a threshold judgment method, and non-uniform data from different types of sensors is converted into a system-wide format. S2. Data feature extraction: transform high-frequency, fragmented raw data into time-based, interpretable indicators. Combine outdoor environmental data such as temperature and wind force to extract load-related features, and extract change features through trend analysis. S3. Abnormal status identification: Based on the extracted feature data, it compares with the normal threshold range to identify the abnormal status of the pipeline network and equipment, triggering early warning or linkage processing. Based on the system design parameters and historical best operating data, it sets the safety range of each indicator and judges the abnormal data. S4. Data output and distribution: The processed standardized data and characteristic indicators need to be output in a targeted manner according to the needs of different downstream modules to support subsequent balance adjustment, energy-saving control and operation and maintenance management.

[0008] Preferably, the construction of a digital twin model of the heating system and the use of the digital twin model for real-time simulation and prediction include the following steps: S10. Digital modeling of the physical system: The physical structure, equipment parameters, and operating rules of the heating system are converted into a digital model at a 1:1 scale. At the same time, the three-dimensional geometric models of the pipeline network drawings, heat source, heat exchange station, and user terminal are imported through BIM technology, and non-geometric key parameters are supplemented. S20. Model dynamic calibration: First, real-time operating data of the physical system is obtained from the data acquisition and monitoring module, and the real-time data is mapped to the corresponding position of the digital model. At the same time, the simulation output value of the virtual model is compared with the real-time measured value of the physical system to locate the deviation item, and the model parameters are adjusted for the deviation item until the deviation is reduced to within the threshold. S30. Real-time simulation mapping: After calibration, the system achieves millisecond-level synchronization with the physical system through a digital twin model, simulating the system's operating status in real time. Furthermore, the system displays the dynamic distribution of flow, pressure, and temperature in real time within the virtual model. S40, multi-scenario prediction and scheme prediction, selects the optimal strategy by virtually rehearsing the effects of different adjustment schemes, and predicts the short-term operating effects. At the same time, it designs a variety of adjustment schemes to meet the needs of hydraulic imbalance and energy-saving optimization. S50, Decision Output and Closed Loop: The pre-simulation results of the digital twin model need to be transformed into executable operation instructions, and the execution effect of the physical system is tracked. At the same time, decision instructions are output to each module. After the physical system executes the instructions, the data acquisition and monitoring module collects the operation data in real time and feeds it back to the digital twin model.

[0009] Preferably, the data acquisition and detection module comprises a hardware acquisition layer, a data transmission layer, and a data preprocessing and access layer. The hardware acquisition layer is responsible for directly contacting the physical heating network and collecting the operating parameters of each node. The data transmission layer is responsible for transmitting the raw data acquired by the hardware acquisition layer to the backend via a wireless network. The data preprocessing and access layer is responsible for processing the transmitted raw data into structured and usable data.

[0010] Preferably, the system-wide monitoring and alarm unit is used to centrally present the dispersed operating data and equipment status, while capturing anomalies and issuing warnings in real time. It is also used to break through the limitations of traditional heating data dispersion and manual inspection. Through centralized visualization of the entire chain data and automatic anomaly capture and hierarchical warning, operation and management personnel can achieve a comprehensive view on one screen and intervene in risks early.

[0011] Preferably, the maintenance work order and equipment management unit are used to transform passive fault repair into proactive preventive maintenance. By systematically managing the maintenance task process and the entire life cycle of the equipment, and through digital control of the entire maintenance task process and refined management of the entire equipment life cycle, the pain points of traditional maintenance, such as passive repair, information chaos, and lack of equipment traceability, are broken. This achieves a shift from post-event maintenance to pre-event prevention, ensuring the stable operation of the heating system and reducing maintenance costs.

[0012] Preferably, the energy consumption analysis and cost management unit is used to transform the invisible energy consumption of the system into analyzable, controllable, and optimizable data assets through multi-dimensional energy consumption statistics, accurate cost accounting, and quantification of energy-saving effects. At the same time, by accurately quantifying system energy consumption and breaking down cost composition, it provides target guidance for hydraulic balance regulation and verifies the actual benefits of energy-saving optimization measures.

[0013] Preferably, the operation strategy configuration and optimization unit is responsible for storing and adjusting the system operation strategy, and adjusting the system's heat supply and flow rate in real time according to the actual heat demand at the end, so as to avoid overheating waste or underheating complaints, and prioritize the selection of low-cost and high-stability energy sources in combination with energy prices and supply stability.

[0014] Preferably, the user service and interaction unit is used to connect the operator and the user, handle requests, transmit information, and transmit user feedback on indoor temperature and heating demand to the system. At the same time, it provides the operation and maintenance team with a real-time fault reporting and adjustment effect feedback channel, replacing the traditional model of manual inspection + user telephone complaints.

[0015] Compared with related technologies, the energy-saving optimization and hydraulic balance regulation system for heating systems provided by this invention has the following beneficial effects: This invention constructs a digital twin model of a heating system and uses this model for real-time simulation and prediction. A 1:1 digital model is established based on the physical structure, equipment parameters, and operating rules. BIM technology is integrated to import three-dimensional geometric information. Real-time operating data is acquired through a data acquisition and monitoring module and mapped to corresponding nodes in the digital model. The simulation output is compared with measured values ​​to dynamically identify deviations and automatically calibrate model parameters. Based on this calibration, millisecond-level real-time synchronization between the digital twin model and the physical system is achieved. The system's spatiotemporal distribution is dynamically simulated and visualized. Furthermore, multi-scenario simulations are conducted based on this model. For hydraulic imbalance regulation and energy-saving optimization needs, quantitative deductions are performed. The system analyzes the short-term operational effects of the same control strategy to select the optimal solution. Finally, the simulation results are transformed into executable control commands, which are then sent to field equipment via a cloud-edge-device collaborative system to drive the physical system to execute. The data acquisition module provides real-time feedback of the actual data after execution, forming a complete closed loop of "perception-modeling-calibration-simulation-decision-execution-feedback". This device reduces the probability that the twin model data will be distorted and the simulation prediction results will deviate from reality, as pipelines are often located underground or in complex outdoor environments where sensors are susceptible to low temperatures and corrosion, and data transmission is prone to delays or interruptions. Furthermore, the system sends data directly to field equipment via a cloud-edge-device collaborative system, eliminating the need for manual relay and reducing delays and operational errors. Attached Figure Description

[0016] Figure 1 A structural block diagram of a heating system energy-saving optimization and hydraulic balance regulation system provided by the present invention; Figure 2 This is a flowchart provided by the present invention for analyzing and processing data collected by the data acquisition and detection module; Figure 3 The flowchart provided by the present invention describes the construction of a digital twin model of a heating system. Detailed Implementation

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0018] Please refer to the following: Figure 1 , Figure 2 as well as Figure 3 ,in, Figure 1 A structural block diagram of a heating system energy-saving optimization and hydraulic balance regulation system provided by the present invention; Figure 2 This is a flowchart provided by the present invention for analyzing and processing data collected by the data acquisition and detection module; Figure 3 The flowchart provided by the present invention describes the construction of a digital twin model of a heating system.

[0019] In the specific implementation process, such as Figures 1 to 3As shown, a heating system energy-saving optimization and hydraulic balance regulation system includes a data acquisition and detection module, which is used to collect heating network operation data in real time and upload the data to the cloud platform via a wireless network. It should be noted that the data acquisition and detection module is the data entry point and sensory nerve ending for the intelligent operation of the heating system. Its core function is to break through the limitations of traditional heating systems, such as manual meter reading, data lag, and monitoring blind spots. By deploying sensors and acquisition devices throughout the entire chain from heat source to pipeline network to heat exchange station to user end, it captures heating network operation data in real time and accurately. Relying on wireless network, it enables uninterrupted data upload to the cloud platform, providing real, real-time, and comprehensive raw data support for subsequent system-wide monitoring, energy consumption analysis, and operation and maintenance management. It is the data cornerstone of the intelligent heating system. Furthermore, the core value of the data acquisition and detection module is to support the monitoring of the entire system, collect temperature and pressure data in real time, ensure the accuracy of energy consumption analysis, enable the triggering of operation and maintenance work orders, and replace manual labor, thereby reducing costs and improving efficiency. The heating network operation data includes operation data at the heat source end, operation data of the main pipeline network, operation data of the heat exchange station, operation data of the building / unit branch pipes, and operation data at the user end; The digital twin and simulation prediction module is used to analyze and process the data collected by the data acquisition and detection module, and at the same time build a digital twin model of the heating system. The digital twin model is then used for real-time simulation and prediction to preview different adjustment schemes and operating strategies. It should be noted that the analysis and processing of data collected by the data acquisition and detection module includes the following steps: S1. Data preprocessing: invalid data is removed from the raw data using a threshold judgment method, and non-uniform data from different types of sensors is converted into a system-wide format. It should be noted that, based on the physical limits or industry standard ranges of data from each component of the heating system, preset thresholds are used. Data exceeding these ranges is identified as outliers and removed to prevent them from interfering with the analysis results. An example of temperature data is provided below. Indoor room temperature: The normal range is usually 5℃-30℃. If -2℃ or 50℃ is collected, it is determined to be an abnormal value caused by sensor failure and is directly rejected. Boiler water supply temperature: Civil heating is usually 40℃-60℃. If 80℃ is collected, it is determined to be a sensor false alarm or equipment failure. The data is removed and the sensor is marked as abnormal, triggering subsequent operation and maintenance reminders. Example of stress data: The operating pressure of the pipeline network is usually 0.2MPa-0.8MPa. If 1.2MPa or 0.05MPa is collected, it is judged as an outlier and removed. The normal data of the adjacent time period is retained to avoid misleading the judgment of whether the pipeline network pressure meets the standard. S2. Data feature extraction: transform high-frequency, fragmented raw data into time-based, interpretable indicators. Combine outdoor environmental data such as temperature and wind force to extract load-related features, and extract change features through trend analysis. S3. Abnormal status identification: Based on the extracted feature data, it compares with the normal threshold range to identify the abnormal status of the pipeline network and equipment, triggering early warning or linkage processing. Based on the system design parameters and historical best operating data, it sets the safety range of each indicator and judges the abnormal data. S4. Data output and distribution: The processed standardized data and characteristic indicators need to be output in a targeted manner according to the needs of different downstream modules to support subsequent balance adjustment, energy-saving control and operation and maintenance management.

[0020] Constructing a digital twin model of a heating system and using it for real-time simulation and prediction includes the following steps: S10. Digital modeling of the physical system: The physical structure, equipment parameters, and operating rules of the heating system are converted into a digital model at a 1:1 scale. At the same time, the three-dimensional geometric models of the pipeline network drawings, heat source, heat exchange station, and user terminal are imported through BIM technology, and non-geometric key parameters are supplemented. S20. Model dynamic calibration: First, real-time operating data of the physical system is obtained from the data acquisition and monitoring module, and the real-time data is mapped to the corresponding position of the digital model. At the same time, the simulation output value of the virtual model is compared with the real-time measured value of the physical system to locate the deviation item, and the model parameters are adjusted for the deviation item until the deviation is reduced to within the threshold. S30. Real-time simulation mapping: After calibration, the system achieves millisecond-level synchronization with the physical system through a digital twin model, simulating the system's operating status in real time. Furthermore, the system displays the dynamic distribution of flow, pressure, and temperature in real time within the virtual model. S40, multi-scenario prediction and scheme prediction, selects the optimal strategy by virtually rehearsing the effects of different adjustment schemes, and predicts the short-term operating effects. At the same time, it designs a variety of adjustment schemes to meet the needs of hydraulic imbalance and energy-saving optimization. S50, Decision Output and Closed Loop: The pre-simulation results of the digital twin model need to be transformed into executable operation instructions, and the execution effect of the physical system needs to be tracked. At the same time, decision instructions are output to each module. After the physical system executes the instructions, the data acquisition and monitoring module collects the operation data in real time and feeds it back to the digital twin model. The whole network balancing module is used to adjust the operating parameters of the heating system based on the output of the digital twin and simulation prediction module. At the same time, the "load prediction - valve opening" mapping model established by reinforcement learning algorithm is incorporated into it. By combining factors such as weather forecast and historical heating patterns, the reinforcement learning algorithm continuously optimizes the valve opening adjustment strategy. It should be noted that traditional network balancing relies on manual on-site valve adjustments, which suffers from problems such as adjustment lag, dependence on experience, and inability to cope with dynamic load changes. This module integrates three technologies to construct a closed-loop control system of prediction, optimization, and execution: Based on digital twin simulation: relying on the simulation results of the hydraulic distribution of the heating network output by the digital twin and simulation prediction module, the heat load prediction value for the next 24 hours can be obtained in advance to identify the risk points of heating network imbalance. Using reinforcement learning models as the core of decision-making: A dynamic mapping model of load prediction and valve opening is established through reinforcement learning algorithms to replace manual experience and automatically calculate the optimal valve regulation scheme; Based on the linkage of multiple factors: integrating weather forecasts, historical heating patterns, and real-time operational data to ensure that the adjustment strategy fits the actual operating scenario; The energy-saving control module is used to generate the target flow rate of cold chain transportation at the station based on big data and machine learning algorithms, and dynamically determine the opening degree of the primary side regulating valve at the station according to the target flow rate, combined with the heating network simulation model and valve regulation characteristics. It should be noted that the core positioning of the energy-saving control module is that traditional heating systems rely heavily on manual experience for flow control, which can easily lead to over-supply during low loads and under-supply during high loads. This module achieves energy-saving control upgrades through two main technical paths: Scientific Target Flow: Based on big data and machine learning algorithms, generate cold chain target flow that is precisely matched with the actual heat load to avoid a disconnect between energy supply and demand; Refined regulation execution: Combining the heating network simulation model and valve regulation characteristics, the optimal opening degree of the primary side regulating valve is dynamically output to ensure that the target flow rate is stably delivered, balancing energy saving and system stability; The terminal control module is used to automatically adjust the valve opening based on the indoor temperature collected by the room temperature detection device; It should be noted that the core function of the terminal control module is to break the traditional extensive mode of unified heating for the entire building and the inability of users to adjust the temperature independently. By linking indoor room temperature detection data with terminal valve control, it achieves closed-loop control by automatically increasing the valve to supplement heat when the room temperature is not up to standard and automatically closing the valve to limit heat when the room temperature exceeds the standard. Ultimately, it achieves the goal of customizing the room temperature according to user needs and avoiding local overheating and waste. It is a key support for improving user comfort and terminal energy saving. The Operations Management module is used to make load forecasts based on historical heating data and weather conditions, and to adjust operating strategies and heating plans in a timely manner. The Operations Management module includes a system-wide monitoring and alarm unit, an operation and maintenance work order and equipment management unit, an energy consumption analysis and cost management unit, an operation strategy configuration and optimization unit, and a user service and interaction unit. The system-wide monitoring and alarm unit is used to centrally present scattered operation data and equipment status, and to capture anomalies and issue warnings in real time. The operation and maintenance work order and equipment management unit is used to transform passive fault repair into proactive preventive operation and maintenance, and to manage operation and maintenance task processes and the entire life cycle of equipment in a systematic way. The energy consumption analysis and cost management unit is used to transform the invisible energy consumption of the system into data assets through multi-dimensional energy consumption statistics, accurate cost accounting, and quantification of energy-saving effects. The operation strategy configuration and optimization unit is responsible for storing and adjusting system operation strategies. The service and interaction unit is used to connect operators and users, handle requests, and transmit information. It should be noted that the data acquisition and detection module consists of a hardware acquisition layer, a data transmission layer, and a data preprocessing and access layer. The hardware acquisition layer is responsible for directly contacting the physical heating network and collecting the operating parameters of each node. The data transmission layer is responsible for transmitting the raw data acquired by the hardware acquisition layer to the back end via a wireless network. The data preprocessing and access layer is responsible for processing the transmitted raw data into structured and usable data. The system-wide monitoring and alarm unit is used to centrally present scattered operational data and equipment status, while capturing anomalies and issuing warnings in real time. It also breaks through the limitations of traditional heating data being scattered and manual inspections. Through centralized visualization of the entire data chain, automatic anomaly capture, and hierarchical warnings, it enables operation and management personnel to view the overall situation on one screen and intervene in risks early. The operation and maintenance work order and equipment management unit are used to transform passive fault repair into proactive preventive operation and maintenance. By systematically managing the operation and maintenance task process and the entire life cycle of equipment, and through digital control of the entire operation and maintenance task process and refined management of the entire life cycle of equipment, it breaks through the pain points of traditional operation and maintenance: passive repair, information chaos, and lack of equipment traceability. It realizes the transformation from post-maintenance to pre-prevention, ensuring the stable operation of the heating system and reducing operation and maintenance costs. The energy consumption analysis and cost management unit is used to transform the invisible energy consumption of the system into analyzable, controllable, and optimizable data assets through multi-dimensional energy consumption statistics, accurate cost accounting, and quantification of energy-saving effects. At the same time, by accurately quantifying system energy consumption and breaking down cost composition, it provides target guidance for hydraulic balance regulation and verifies the actual benefits of energy-saving optimization measures. The operation strategy configuration and optimization unit is responsible for storing and adjusting the system operation strategy, and adjusting the system's heat supply and flow in real time according to the actual heat demand at the end, so as to avoid overheating waste or underheating complaints. Combining energy prices and supply stability, it prioritizes low-cost and high-stability energy sources. The User Service and Interaction Unit connects operators and users, handles requests, transmits information, and transmits user feedback on indoor temperature and heating demand to the system. It also provides the maintenance team with a real-time fault reporting and adjustment effect feedback channel, replacing the traditional model of manual inspection + user telephone complaints. The fault diagnosis and early warning module is used to diagnose potential faults in the pipeline network in real time by analyzing pipeline network operation data. It should be noted that the fault diagnosis and early warning module is a fault monitoring sentinel for the heating system to ensure safe and stable operation and reduce sudden outages. Its core function is to break the limitations of traditional heating faults that are discovered through manual inspections and reactive repairs after the fact. Through in-depth analysis of real-time operating data of pipeline flow, pressure, temperature, and energy consumption, combined with historical fault patterns and equipment characteristic models, it can identify hidden dangers and obvious faults in the pipeline in real time and issue early warnings, giving maintenance personnel time to take proactive measures and avoiding large-scale heating outages and soaring maintenance costs caused by the escalation of faults.

[0021] The circuits and controls involved in this invention are all existing technologies and will not be described in detail here.

[0022] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A heating system energy saving optimization and hydraulic balance adjustment system, characterized in that, It includes a data acquisition and detection module, which is used to collect heating network operation data in real time and upload the data to the cloud platform via wireless network; The digital twin and simulation prediction module is used to analyze and process the data collected by the data acquisition and detection module, and at the same time build a digital twin model of the heating system. The digital twin model is then used for real-time simulation and prediction to preview different adjustment schemes and operating strategies. The whole network balancing module is used to adjust the operating parameters of the heating system based on the output of the digital twin and simulation prediction module. At the same time, the "load prediction - valve opening" mapping model established by reinforcement learning algorithm is incorporated into it. By combining factors such as weather forecast and historical heating patterns, the reinforcement learning algorithm continuously optimizes the valve opening adjustment strategy. The energy-saving control module is used to generate the target flow rate of cold chain transportation at the station based on big data and machine learning algorithms, and dynamically determine the opening degree of the primary side regulating valve at the station according to the target flow rate, combined with the heating network simulation model and valve regulation characteristics. The terminal control module is used to automatically adjust the valve opening based on the indoor temperature collected by the room temperature detection device; The operation management module is used to make load forecasts based on historical heating data and weather conditions, and to adjust operation strategies and heating plans in a timely manner. This module includes a system-wide monitoring and alarm unit, a maintenance work order and equipment management unit, an energy consumption analysis and cost management unit, an operation strategy configuration and optimization unit, and a user service and interaction unit. The system-wide monitoring and alarm unit centrally presents scattered operation data and equipment status, while simultaneously capturing anomalies and issuing warnings in real time. The maintenance work order and equipment management unit transforms reactive fault repair into proactive preventative maintenance by systematically managing maintenance task processes and the entire equipment lifecycle. The energy consumption analysis and cost management unit transforms invisible energy consumption into data assets through multi-dimensional energy consumption statistics, accurate cost accounting, and quantification of energy-saving effects. The operation strategy configuration and optimization unit is responsible for storing and adjusting system operation strategies. The service and interaction unit connects operators and users, handles requests, and transmits information. The fault diagnosis and early warning module is used to diagnose potential faults in the pipeline network in real time by analyzing pipeline network operation data.

2. The heating system energy-saving optimization and hydraulic balance regulation system according to claim 1, characterized in that, The analysis and processing of the data collected by the data acquisition and detection module includes the following steps: S1. Data preprocessing: invalid data is removed from the raw data using a threshold judgment method, and non-uniform data from different types of sensors is converted into a system-wide format. S2. Data feature extraction: transform high-frequency, fragmented raw data into time-based, interpretable indicators. Combine outdoor environmental data such as temperature and wind force to extract load-related features, and extract change features through trend analysis. S3. Abnormal status identification: Based on the extracted feature data, it compares with the normal threshold range to identify the abnormal status of the pipeline network and equipment, triggering early warning or linkage processing. Based on the system design parameters and historical best operating data, it sets the safety range of each indicator and judges the abnormal data. S4. Data output and distribution: The processed standardized data and characteristic indicators need to be output in a targeted manner according to the needs of different downstream modules to support subsequent balance adjustment, energy-saving control and operation and maintenance management.

3. The heating system energy-saving optimization and hydraulic balance regulation system according to claim 2, characterized in that, The process of constructing a digital twin model of the heating system and using the digital twin model for real-time simulation and prediction includes the following steps: S10. Digital modeling of the physical system: The physical structure, equipment parameters, and operating rules of the heating system are converted into a digital model at a 1:1 scale. At the same time, the three-dimensional geometric models of the pipeline network drawings, heat source, heat exchange station, and user terminal are imported through BIM technology, and non-geometric key parameters are supplemented. S20. Model dynamic calibration: First, real-time operating data of the physical system is obtained from the data acquisition and monitoring module, and the real-time data is mapped to the corresponding position of the digital model. At the same time, the simulation output value of the virtual model is compared with the real-time measured value of the physical system to locate the deviation item, and the model parameters are adjusted for the deviation item until the deviation is reduced to within the threshold. S30. Real-time simulation mapping: After calibration, the system achieves millisecond-level synchronization with the physical system through a digital twin model, simulating the system's operating status in real time. Furthermore, the system displays the dynamic distribution of flow, pressure, and temperature in real time within the virtual model. S40, multi-scenario prediction and scheme prediction, selects the optimal strategy by virtually rehearsing the effects of different adjustment schemes, and predicts the short-term operating effects. At the same time, it designs a variety of adjustment schemes to meet the needs of hydraulic imbalance and energy-saving optimization. S50, Decision Output and Closed Loop: The pre-simulation results of the digital twin model need to be transformed into executable operation instructions, and the execution effect of the physical system is tracked. At the same time, decision instructions are output to each module. After the physical system executes the instructions, the data acquisition and monitoring module collects the operation data in real time and feeds it back to the digital twin model.

4. The heating system energy-saving optimization and hydraulic balance regulation system according to claim 3, characterized in that, The data acquisition and detection module comprises a hardware acquisition layer, a data transmission layer, and a data preprocessing and access layer. The hardware acquisition layer is responsible for directly contacting the physical heating network and collecting the operating parameters of each node. The data transmission layer is responsible for transmitting the raw data acquired by the hardware acquisition layer to the backend via a wireless network. The data preprocessing and access layer is responsible for processing the transmitted raw data into structured and usable data.

5. The heating system energy-saving optimization and hydraulic balance regulation system according to claim 4, characterized in that, The system-wide monitoring and alarm unit is used to centrally present scattered operational data and equipment status, while capturing anomalies and issuing warnings in real time. It also breaks through the limitations of traditional heating data being scattered and requiring manual inspections. Through centralized visualization of the entire data chain, automatic anomaly capture, and tiered warnings, it enables operation and management personnel to view the overall situation on one screen and intervene in risks early.

6. The heating system energy-saving optimization and hydraulic balance regulation system according to claim 5, characterized in that, The aforementioned maintenance work orders and equipment management unit are used to transform passive fault repair into proactive preventive maintenance. By systematically managing maintenance task processes and the entire lifecycle of equipment, and through digital control of the entire maintenance task process and refined management of the entire equipment lifecycle, it breaks through the pain points of traditional maintenance, such as passive repair, information chaos, and lack of equipment traceability. It realizes the transformation from post-maintenance to pre-prevention, ensuring the stable operation of the heating system and reducing maintenance costs.

7. The heating system energy-saving optimization and hydraulic balance regulation system according to claim 6, characterized in that, The energy consumption analysis and cost management unit is used to transform the invisible energy consumption of the system into analyzable, controllable, and optimizable data assets through multi-dimensional energy consumption statistics, accurate cost accounting, and quantification of energy-saving effects. At the same time, by accurately quantifying system energy consumption and breaking down cost composition, it provides target guidance for hydraulic balance regulation and verifies the actual benefits of energy-saving optimization measures.

8. The heating system energy-saving optimization and hydraulic balance regulation system according to claim 7, characterized in that, The operation strategy configuration and optimization unit is responsible for storing and adjusting the system operation strategy, and adjusting the system's heat supply and flow rate in real time according to the actual heat demand at the end, so as to avoid overheating waste or underheating complaints, and prioritize the selection of low-cost and high-stability energy sources in combination with energy prices and supply stability.

9. The heating system energy-saving optimization and hydraulic balance regulation system according to claim 8, characterized in that, The user service and interaction unit is used to connect operators and users, handle requests, transmit information, and transmit user feedback on indoor temperature and heating demand to the system. At the same time, it provides the operation and maintenance team with a real-time fault reporting and adjustment effect feedback channel, replacing the traditional model of manual inspection + user telephone complaints.

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